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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/225134
MXgap: A MXene Learning Tool for Bandgap Prediction
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The increasing demand for clean and renewable energy has intensified the exploration of advanced materials for efficient photocatalysis, particularly for water splitting applications. Among these materials, MXenes, a family of two-dimensional (2D) transition metal carbides and nitrides, have shown great promise. This study leverages machine learning (ML) to address the resource-intensive process of predicting the bandgap of MXenes, which is critical for their photocatalytic performance. Using an extensive data set of 4356 MXene structures, we trained multiple ML models and developed a robust classifier-regressor pipeline that achieves a classification accuracy of 92% and a mean absolute error (MAE) of 0.17 eV for bandgap prediction. This framework, implemented in an open-source Python package, MXgap, has been applied to screen 396 La-based MXenes, identifying six promising candidates with suitable band alignments for water splitting whose optical properties were further explored via optical absorption and solar to-hydrogen (STH) efficiency. These findings demonstrate the potential of ML to accelerate MXene discovery and optimization for energy applications.
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ONTIVEROS CRUZ, Diego, et al. MXgap: A MXene Learning Tool for Bandgap Prediction. ACS Catalysis. 2025. Vol. 15, num. 14403-14413. ISSN 2155-5435. [consulted: 13 of June of 2026]. Available at: https://hdl.handle.net/2445/225134